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<p>In data mining research, streaming data analysis has emerged as a critical computational paradigm requiring real-time processing capabilities to ensure temporal relevance and practical applicability. However, dynamic data stream environments commonly exhibit complex challenges including multi-class imbalance phenomena with time-varying distribution ratios, and concept drift characteristics that collectively undermine the stability and accuracy of conventional classification frameworks. To address these interdisciplinary challenges, this study presents SCWE+, an innovative data stream classification algorithm specifically designed for multi-class imbalance scenarios with concept drift adaptation. The methodology comprises three core components: (1) A sample classification difficulty weighting mechanism integrating margin-based sample evaluation with supervised negative margin rescue loss, which strategically enhances model focus on both easily misclassified instances and minority class distributions through adaptive attention allocation; (2) A dynamic ensemble selection framework incorporating adaptive plasticity reward (APR), which implements hierarchical sliding window analysis across sample, class-specific, and difficulty-stratified dimensions to generate context-aware classifier performance evaluations and optimize prediction ensembles through weighted aggregation; (3) An expert validation architecture that constructs class-specialized expert groups by selecting high-performance classifiers from the classifier pool for specific class domains, enabling post-ensemble prediction verification to mitigate misclassification risks in low-confidence predictions. Comprehensive empirical evaluations were conducted across diverse synthetic and real-world data stream benchmarks, demonstrating statistically significant performance improvements compared to nine state-of-the-art data stream classification algorithms under varying imbalance ratios and concept drift scenarios.</p>

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SCWE+: A multi-class imbalanced drifting data stream classification algorithm based on sample difficulty weighting and dynamic ensemble selection

  • 期刊论文

  • 发表日期：

    2026年3月11日

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  • Meng Han,
  • Shineng Zhu,
  • Ang Li,
  • Jian Ding

摘要

In data mining research, streaming data analysis has emerged as a critical computational paradigm requiring real-time processing capabilities to ensure temporal relevance and practical applicability. However, dynamic data stream environments commonly exhibit complex challenges including multi-class imbalance phenomena with time-varying distribution ratios, and concept drift characteristics that collectively undermine the stability and accuracy of conventional classification frameworks. To address these interdisciplinary challenges, this study presents SCWE+, an innovative data stream classification algorithm specifically designed for multi-class imbalance scenarios with concept drift adaptation. The methodology comprises three core components: (1) A sample classification difficulty weighting mechanism integrating margin-based sample evaluation with supervised negative margin rescue loss, which strategically enhances model focus on both easily misclassified instances and minority class distributions through adaptive attention allocation; (2) A dynamic ensemble selection framework incorporating adaptive plasticity reward (APR), which implements hierarchical sliding window analysis across sample, class-specific, and difficulty-stratified dimensions to generate context-aware classifier performance evaluations and optimize prediction ensembles through weighted aggregation; (3) An expert validation architecture that constructs class-specialized expert groups by selecting high-performance classifiers from the classifier pool for specific class domains, enabling post-ensemble prediction verification to mitigate misclassification risks in low-confidence predictions. Comprehensive empirical evaluations were conducted across diverse synthetic and real-world data stream benchmarks, demonstrating statistically significant performance improvements compared to nine state-of-the-art data stream classification algorithms under varying imbalance ratios and concept drift scenarios.